Somin Kim, Robert Feldt, Shin Yoo · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28187 · open-access PDF
测试基于LLM的软件是昂贵的:每个输入需要多个采样响应,每一个由一个昂贵的方言评判。此外,每一个提示或模型改变要求重新测试。前一代的足够性措施通过基于模型内部状态的失败风险排序输入来削减此成本。然而,艺术的状态,克洛托,只有通过参考输入的模型,其隐藏状态的代表性没有说出为什么输入失败。我们介绍了Foreword,一个基于两种想法的框架:从通过和失败的参考中学习,并通过雅各比亚镜头(J-lens)表示输入
Machine translation (Meta M2M100); the English original follows.
Testing LLM-based software is expensive: each input needs several sampled responses, each judged by a costly oracle. Further, every prompt or model change calls for re-testing. Pre-generation adequacy measures cut this cost by ranking inputs by failure risk based on the model's internal state. However, the state of the art, Clotho, models only passing reference inputs, and its hidden-state representation says nothing about why inputs fail. We present Foreword, a framework built on two ideas: learn from both passing and failing references, and represent inputs through the Jacobian lens (J-lens)
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